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Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology

arXiv 2507.07983 ↗First posted 10 Jul 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Large language models (LLMs) show promise for supporting clinical decision-making in complex fields such as rheumatology. Our evaluation shows that smaller language models (SLMs), combined with retrieval-augmented generation (RAG), achieve higher diagnostic and therapeutic performance than larger models, while requiring substantially less energy and enabling cost-efficient, local deployment. These features are attractive for resource-limited healthcare. However, expert oversight remains essential, as no model consistently reached specialist-level accuracy in rheumatology.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 10 Jul 2025.

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8 of 26 references.

References · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  5. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directionscited by 70
  7. 2023Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understandingcited by 40
  8. 2024Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicinecited by 3

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  1. 2025Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis
  2. 2025Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
  3. 2025Retrieval-Augmented Framework for LLM-Based Clinical Decision Support
  4. 2025BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
  5. 2026A primer on evaluation methods for large language models in healthcare
  6. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  7. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  8. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines